Related papers: A robust morphological classification method for g…

Stars form in molecular complexes that are visible as giant clouds ($\sim 10^{5-6} \mathrm{M}_\odot$) in nearby galaxies and as giant clumps ($\sim 10^{8-9}\mathrm{M}_\odot$) in galaxies at redshifts $z\approx1$$-$$3$. Theoretical…

We select and characterise a sample of massive (log(M$_{*}/$M$_{\odot})>10.6$) quiescent galaxies (QGs) at $3<z<5$ in the latest COSMOS2020 catalogue. QGs are selected using a new rest-frame colour selection method, based on their…

We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21,…

We applied computer analysis to classify the broad morphological type of ~3,000,000 SDSS galaxies. The catalog provides for each galaxy the DR8 object ID, right ascension, declination, and the certainty of the automatic classification to…

Thanks to decades of observations using the Hubble Space Telescope (HST), the structure of galaxies at redshift $z>2$ has been widely studied in the rest-frame ultraviolet regime, which traces recent star formation from young stellar…

Galaxy morphology, a key tracer of the evolution of a galaxy's physical structure, has motivated extensive research on machine learning techniques for efficient and accurate galaxy classification. The emergence of quantum computers has…

We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for $\sim 8$ million galaxies in the Hyper Suprime-Cam (HSC) Wide survey with $z \leq 0.75$ and $m \leq…

The sensitivity and wide area reached by ongoing and future wide-field optical surveys allows for the detection of an increasing number of galaxy clusters uniquely through their weak lensing (WL) signal. This motivates the development of…

We analyze the optical morphologies of galaxies in the IllustrisTNG simulation at $z\sim0$ with a Convolutional Neural Network trained on visual morphologies in the Sloan Digital Sky Survey. We generate mock SDSS images of a mass complete…

We present a supervised machine learning classification of sources from the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) Pathfinder datasets: miniJPAS and J-NEP. Leveraging crossmatches with spectroscopic and…

Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolutional neural networks have proven to be highly effective in…

The colour bimodality of galaxies provides an empirical basis for theories of galaxy evolution. However, the balance of processes that begets this bimodality has not yet been constrained. A more detailed view of the galaxy population is…

With the advent of JWST, we can probe the rest-frame optical emission of galaxies at $z>3$ with high sensitivity and spatial resolution, making it possible to accurately characterise red, optically-faint galaxies and thus move towards a…

Observations of high-redshift galaxies provide a critical direct test to the theories of early galaxy formation, yet to date, only three have been spectroscopically confirmed at $z>12$. Due to strong gravitational lensing over a wide area,…

We present the first nonparametric morphological analysis of a set of spiral galaxies from UV to submm wavelengths. Our study is based on high-quality multi-wavelength imaging for nine well-resolved spiral galaxies from the DustPedia…

Low-mass or dwarf galaxies (M$_{\ast}<10^{9}$ M${\odot}$) are abundant in the Universe, yet their formation and evolution remain poorly understood. Their enhanced sensitivity to feedback from star formation and active galactic nuclei (AGN)…

Multi-label image classification presents a challenging task in many domains, including computer vision and medical imaging. Recent advancements have introduced graph-based and transformer-based methods to improve performance and capture…

We measure the number density and field-to-field variance of massive quiescent galaxies at $z\sim3$ - 8 using the JWST/NIRCam pure-parallel imaging survey PANORAMIC together with archival observations, covering an area of 0.28 deg$^2$…

Waste classification is crucial for improving processing efficiency and reducing environmental pollution. Supervised deep learning methods are commonly used for automated waste classification, but they rely heavily on large labeled…

Upcoming large imaging surveys will allow detailed studies of the structure and morphology of galaxies aimed at addressing how galaxies form and evolve. Computational approaches are needed to characterize their morphologies over large…